Pedestrian Attributes Recognition Paper List 

2018-12-22 22:08:55

[Note] you may also check the updated version of this blog from my github: https://github.com/wangxiao5791509/Pedestrian-Attribute-Recognition-Paper-List

The survey paper of pedestrian attribute recognition can be found from:

Pedestrian Attribute Recognition: A Survey, Xiao Wang, Shaofei Zheng, Rui Yang, Bin Luo, Jin Tang, https://arxiv.org/abs/1901.07474

Project-page: https://sites.google.com/view/ahu-pedestrianattributes/

High Resolution version:

(Google Drive): https://drive.google.com/open?id=1DbuvkgNm4WltdURxhP2KdF2VSRQr2E4D

(Baidu Yun): Link：https://pan.baidu.com/s/1PDyFpaWoS4dezUE-onKG8A Code：oe7g

Dataset:

1. PETA Dataset: http://mmlab.ie.cuhk.edu.hk/projects/PETA.html
2. RAP Dataset: http://rap.idealtest.org/
3. PA-100K Dataset: https://drive.google.com/drive/folders/0B5_Ra3JsEOyOUlhKM0VPZ1ZWR2M
4. WIDER Attribute Dataset: http://mmlab.ie.cuhk.edu.hk/projects/WIDERAttribute.html
5. Database of Human Attributes (HAT): https://jurie.users.greyc.fr/datasets/hat.html
6. Market-1501_Attribute: https://github.com/vana77/Market-1501_Attribute
7. DukeMTMC-Attribute: https://github.com/vana77/DukeMTMC-attribute
8. Clothing Attributes Dataset: https://purl.stanford.edu/tb980qz1002
9. Parse27k Dataset: https://www.vision.rwth-aachen.de/page/parse27k
10. RAP 2.0 Dataset: https://drive.google.com/file/d/1hoPIB5NJKf3YGMvLFZnIYG5JDcZTxHph/view
11. CRP Dataset: http://www.vision.caltech.edu/~dhall/projects/CRP/
12. APis dataset: http://www.cbsr.ia.ac.cn/english/APiS-1.0-Database.html. (Failed)
13. Berkeley-Attributes of People dataset: https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/shape/poselets/
14. Deepfashion dataset: http://mmlab.ie.cuhk.edu.hk/projects/DeepFashion.html
15. Video-Based PAR dataset: https://github.com/yuange250/MARS-Attribute

Code:

A baseline model ( pytorch implementation ) for person attribute recognition task, training and testing on Market1501-attribute and DukeMTMC-reID-attribute dataset. https://github.com/hyk1996/Person-Attribute-Recognition-MarketDuke

DeepMAR from "Multi-attribute learning for pedestrian attribute recognition": https://github.com/kyu-sz/DeepMAR_deploy

Multi-attribute Learning for Pedestrian Attribute Recognition in Surveillance Scenarios, Dangwei Li and Xiaotang Chen and Kaiqi Huang, ACPR 2015: https://github.com/dangweili/pedestrian-attribute-recognition-pytorch

Multi-label Image Recognition by Recurrently Discovering Attentional Regions (Pytorch implementation): https://github.com/James-Yip/AttentionImageClass

A Richly Annotated Pedestrian Dataset for Person Retrieval in Real Surveillance Scenarios: Li, Dangwei and Zhang, Zhang and Chen, Xiaotang and Huang, Kaiqi, IEEE Transactions on Image Processing 2019: https://github.com/dangweili/RAP

PatchIt (BMVC-2016): https://github.com/psudowe/patchit

PANDA (CVOR-2014): https://github.com/facebookarchive/pose-aligned-deep-networks

HydraPlus-Net (ICCV-2017): https://github.com/xh-liu/HydraPlus-Net

WPAL-network (BMVC-2014) https://github.com/YangZhou1994/WPAL-network

Deep Imbalanced Attribute Classification using Visual Attention Aggregation (ECCV-2018): https://github.com/cvcode18/imbalanced_learning

The paper list of person attribute recognition:

[1] Deng, Yubin, Ping Luo, Chen Change Loy, and Xiaoou Tang. "Pedestrian attribute recognition at far distance." In Proceedings of the 22nd ACM international conference on Multimedia, pp. 789-792. ACM, 2014. Paper Project Page

[2] Sudowe, Patrick, Hannah Spitzer, and Bastian Leibe. "Person attribute recognition with a jointly-trained holistic cnn model." In Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 87-95. 2015. Paper
Project Page

[3] Zhu, Jianqing, Shengcai Liao, Zhen Lei, Dong Yi, and Stan Li. "Pedestrian attribute classification in surveillance: Database and evaluation." In Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 331-338. 2013. Paper

[4] Li, Dangwei, Zhang Zhang, Xiaotang Chen, Haibin Ling, and Kaiqi Huang. "A richly annotated dataset for pedestrian attribute recognition." arXiv preprint arXiv:1603.07054 (2016). Paper Project Page

[5] Fabbri, Matteo, Simone Calderara, and Rita Cucchiara. "Generative adversarial models for people attribute recognition in surveillance." In Advanced Video and Signal Based Surveillance (AVSS), 2017 14th IEEE International Conference on, pp. 1-6. IEEE, 2017. Paper

[6] Guo, Qi, Ce Zhu, Zhiqiang Xia, Zhengtao Wang, and Yipeng Liu. "Attribute-controlled face photo synthesis from simple line drawing." In Image Processing (ICIP), 2017 IEEE International Conference on, pp. 2946-2950. IEEE, 2017. Paper

[7] Yan, Xinchen, Jimei Yang, Kihyuk Sohn, and Honglak Lee. "Attribute2image: Conditional image generation from visual attributes." In European Conference on Computer Vision, pp. 776-791. Springer, Cham, 2016.

[8] Hand, Emily M., and Rama Chellappa. "Attributes for Improved Attributes: A Multi-Task Network Utilizing Implicit and Explicit Relationships for Facial Attribute Classification." In AAAI, pp. 4068-4074. 2017.

[9] Yang, L. , Zhu, L. , Wei, Y. , Liang, S. , & Tan, P. . (2016). Attribute recognition from adaptive parts.

[10] Park, Seyoung, and Song-Chun Zhu. "Attributed grammars for joint estimation of human attributes, part and pose." In Proceedings of the IEEE International Conference on Computer Vision, pp. 2372-2380. 2015.

[11] Gkioxari, Georgia, Ross Girshick, and Jitendra Malik. "Actions and attributes from wholes and parts." In Proceedings of the IEEE International Conference on Computer Vision, pp. 2470-2478. 2015.

[12] Li, Yining, Chen Huang, Chen Change Loy, and Xiaoou Tang. "Human attribute recognition by deep hierarchical contexts." In European Conference on Computer Vision, pp. 684-700. Springer, Cham, 2016. Paper

[13] Zhang, Ning, Manohar Paluri, Marc'Aurelio Ranzato, Trevor Darrell, and Lubomir Bourdev. "Panda: Pose aligned networks for deep attribute modeling." In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1637-1644. 2014. Paper Code

[14] Bourdev, Lubomir, Subhransu Maji, and Jitendra Malik. "Describing people: A poselet-based approach to attribute classification." In Computer Vision (ICCV), 2011 IEEE International Conference on, pp. 1543-1550. IEEE, 2011.

[15] Li, Dangwei, Xiaotang Chen, Zhang Zhang, and Kaiqi Huang. "Pose Guided Deep Model for Pedestrian Attribute Recognition in Surveillance Scenarios." In 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1-6. IEEE, 2018. Paper: http://dangweili.github.io/misc/pdfs/icme18.pdf

[16] Wang, Jingya, Xiatian Zhu, Shaogang Gong, and Wei Li. "Attribute Recognition by Joint Recurrent Learning of Context and Correlation." In Computer Vision (ICCV), 2017 IEEE International Conference on, pp. 531-540. IEEE, 2017.

[17] Chen, Tianshui, Zhouxia Wang, Guanbin Li, and Liang Lin. "Recurrent Attentional Reinforcement Learning for Multi-label Image Recognition." AAAI2018 Paper: http://www.linliang.net/wp-content/uploads/2018/01/AAAI2018_AttentionRL.pdf

[18] Wang, Z., Chen, T., Li, G., Xu, R., & Lin, L. (2017, October). Multi-label Image Recognition by Recurrently Discovering Attentional Regions. In Computer Vision (ICCV), 2017 IEEE International Conference on (pp. 464-472). IEEE. Paper: http://openaccess.thecvf.com/content_ICCV_2017/papers/Wang_Multi-Label_Image_Recognition_ICCV_2017_paper.pdf Code: https://github.com/James-Yip/AttentionImageClass

[19] Wang, Jiang, Yi Yang, Junhua Mao, Zhiheng Huang, Chang Huang, and Wei Xu. "Cnn-rnn: A unified framework for multi-label image classification." In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2285-2294. 2016.

[20] Trigeorgis, George, Konstantinos Bousmalis, Stefanos Zafeiriou, and Björn W. Schuller. "A deep matrix factorization method for learning attribute representations." IEEE transactions on pattern analysis and machine intelligence 39, no. 3 (2017): 417-429.

[21] Lampert, Christoph H., Hannes Nickisch, and Stefan Harmeling. "Attribute-based classification for zero-shot visual object categorization." IEEE Transactions on Pattern Analysis and Machine Intelligence 36, no. 3 (2014): 453-465.

[22] Lin, Yutian, Liang Zheng, Zhedong Zheng, Yu Wu, and Yi Yang. "Improving person re-identification by attribute and identity learning." arXiv preprint arXiv:1703.07220 (2017).

[23] Liu, Xihui, Haiyu Zhao, Maoqing Tian, Lu Sheng, Jing Shao, Shuai Yi, Junjie Yan, and Xiaogang Wang. "Hydraplus-net: Attentive deep features for pedestrian analysis." arXiv preprint arXiv:1709.09930 (2017). Code: https://github.com/xh-liu/HydraPlus-Net

[24] Fouhey, David F., Abhinav Gupta, and Andrew Zisserman. "3D shape attributes." In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1516-1524. 2016.

[25] Fouhey, David F., Abhinav Gupta, and Andrew Zisserman. "Understanding higher-order shape via 3D shape attributes." IEEE TPAMI (2017).

[26] Zhou, Yang, Kai Yu, Biao Leng, Zhang Zhang, Dangwei Li, Kaiqi Huang, Bailan Feng, and Chunfeng Yao. "Weakly-supervised learning of mid-level features for pedestrian attribute recognition and localization." In BMVC. 2017. Paper: Code: https://github.com/YangZhou1994/WPAL-network

[27] Wang, Jing, Yu Cheng, and Rogerio Schmidt Feris. "Walk and learn: Facial attribute representation learning from egocentric video and contextual data." In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2295-2304. 2016.

[28] Su, Jong-Chyi, * Wu, Huaizu Jiang, and Subhransu Maji. "Reasoning about fine-grained attribute phrases using reference games." arXiv preprint arXiv:1708.08874 (2017).

[29] Dong, Qi, Shaogang Gong, and Xiatian Zhu. "Multi-task curriculum transfer deep learning of clothing attributes." In Applications of Computer Vision (WACV), 2017 IEEE Winter Conference on, pp. 520-529. IEEE, 2017.

[30] Li, Dangwei, Xiaotang Chen, and Kaiqi Huang. "Multi-attribute learning for pedestrian attribute recognition in surveillance scenarios." In Pattern Recognition (ACPR), 2015 3rd IAPR Asian Conference on, pp. 111-115. IEEE, 2015.

[31] Kalayeh, Mahdi M., Boqing Gong, and Mubarak Shah. "Improving facial attribute prediction using semantic segmentation." In Computer Vision and Pattern Recognition (CVPR), 2017 IEEE Conference on, pp. 4227-4235. IEEE, 2017.

[32] Lu, Yongxi, Abhishek Kumar, Shuangfei Zhai, Yu Cheng, Tara Javidi, and Rogério Schmidt Feris. "Fully-adaptive Feature Sharing in Multi-Task Networks with Applications in Person Attribute Classification." In CVPR, vol. 1, no. 2, p. 6. 2017.

[32] Sarafianos, Nikolaos, Xiang Xu, and Ioannis A. Kakadiaris. "Deep Imbalanced Attribute Classification using Visual Attention Aggregation." In Proceedings of the European Conference on Computer Vision (ECCV), pp. 680-697. 2018. Paper: http://openaccess.thecvf.com/content_ECCV_2018/papers/Nikolaos_Sarafianos_Deep_Imbalanced_Attribute_ECCV_2018_paper.pdf Code: https://github.com/cvcode18/imbalanced_learning

[33] Li, Mu, Wangmeng Zuo, and David Zhang. "Deep identity-aware transfer of facial attributes." arXiv preprint arXiv:1610.05586 (2016).

[34] He, Keke, Zhanxiong Wang, Yanwei Fu, Rui Feng, Yu-Gang Jiang, and Xiangyang Xue. "Adaptively Weighted Multi-task Deep Network for Person Attribute Classification." In Proceedings of the 2017 ACM on Multimedia Conference, pp. 1636-1644. ACM, 2017. https://dl.acm.org/citation.cfm?id=3123424

[35] Sarfraz, M. Saquib, Arne Schumann, Yan Wang, and Rainer Stiefelhagen. "Deep View-Sensitive Pedestrian Attribute Inference in an end-to-end Model." arXiv preprint arXiv:1707.06089 (2017). https://arxiv.org/pdf/1707.06089.pdf

[36] Liu, Xihui, Haiyu Zhao, Maoqing Tian, Lu Sheng, Jing Shao, Shuai Yi, Junjie Yan, and Xiaogang Wang. "HydraPlus-Net: Attentive Deep Features for Pedestrian Analysis." In Proceedings of the IEEE International Conference on Computer Vision, pp. 350-359. 2017. Paper: http://openaccess.thecvf.com/content_ICCV_2017/papers/Liu_HydraPlus-Net_Attentive_Deep_ICCV_2017_paper.pdf Code: https://github.com/xh-liu/HydraPlus-Net

[37] Xin Zhao; Liufang Sang; guiguang ding; Jungong Han; Na Di; Chenggang Yan, Recurrent Attention Model for Pedestrian Attribute Recognition, AAAI-2019

[38] Qiaozhe Li*; Xin Zhao; Ran He; KAIQI HUANG, Visual-semantic Graph Reasoning for Pedestrian Attribute Recognition, AAAI-2019

[39] Zhao, Xin, Liufang Sang, Guiguang Ding, Yuchen Guo, and Xiaoming Jin. "Grouping Attribute Recognition for Pedestrian with Joint Recurrent Learning." In IJCAI, pp. 3177-3183. 2018. Paper

[40] Diba, Ali, Ali Mohammad Pazandeh, Hamed Pirsiavash, and Luc Van Gool. "Deepcamp: Deep convolutional action & attribute mid-level patterns." In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3557-3565. 2016. Paper

[41] Park, Seyoung, Bruce Xiaohan Nie, and Song-Chun Zhu. "Attribute and-or grammar for joint parsing of human pose, parts and attributes." IEEE transactions on pattern analysis and machine intelligence 40, no. 7 (2018): 1555-1569. Paper

[42] Guo, Hao, Xiaochuan Fan, and Song Wang. "Human attribute recognition by refining attention heat map." Pattern Recognition Letters 94 (2017): 38-45. Paper

[43] Sarafianos, Nikolaos, Theodore Giannakopoulos, Christophoros Nikou, and Ioannis A. Kakadiaris. "Curriculum Learning for Multi-Task Classification of Visual Attributes." In Proceedings of the IEEE International Conference on Computer Vision, pp. 2608-2615. 2017. Paper

[44] Sarafianos, Nikolaos, Theodoros Giannakopoulos, Christophoros Nikou, and Ioannis A. Kakadiaris. "Curriculum learning of visual attribute clusters for multi-task classification." Pattern Recognition 80 (2018): 94-108. Paper

[45] Li, Dangwei, Zhang Zhang, Xiaotang Chen, and Kaiqi Huang. "A richly annotated pedestrian dataset for person retrieval in real surveillance scenarios." IEEE transactions on image processing 28, no. 4 (2019): 1575-1590. Paper Code

[46] Liu, Hao, Jingjing Wu, Jianguo Jiang, Meibin Qi, and Ren Bo. "Sequence-based Person Attribute Recognition with Joint CTC-Attention Model." arXiv preprint arXiv:1811.08115 (2018). Paper

[47] Joo, Jungseock, Shuo Wang, and Song-Chun Zhu. "Human attribute recognition by rich appearance dictionary." In Proceedings of the IEEE International Conference on Computer Vision, pp. 721-728. 2013. Paper

[48] Sharma, G. and Jurie, F., 2011, August. Learning discriminative spatial representation for image classification. In BMVC 2011-British Machine Vision Conference (pp. 1-11). BMVA Press. Paper

[49] Hall, David, and Pietro Perona. "Fine-grained classification of pedestrians in video: Benchmark and state of the art." In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5482-5491. 2015. Paper

[50] Liu, P., Liu, X., Yan, J., & Shao, J. (2018). Localization guided learning for pedestrian attribute recognition. arXiv preprint arXiv:1808.09102. BMVC-paper

[51] Chen, Huizhong, Andrew Gallagher, and Bernd Girod. "Describing clothing by semantic attributes." European conference on computer vision. Springer, Berlin, Heidelberg, 2012.Paper

[52] Deng, Y., Luo, P., Loy, C. C., & Tang, X. (2015). Learning to recognize pedestrian attribute. arXiv preprint arXiv:1501.00901.Paper

[53] Zhu, Jianqing, et al. "Multi-label convolutional neural network based pedestrian attribute classification." Image and Vision Computing 58 (2017): 224-229. Paper

[54] Pedestrian Attribute Detection using CNN, Standford University, CS231n, 2016, Agrim Gupta and Jayanth Ramesh, Paper: http://cs231n.stanford.edu/reports/2016/pdfs/255_Report.pdf

[55] Abdulnabi, Abrar H., Gang Wang, Jiwen Lu, and Kui Jia. "Multi-task CNN model for attribute prediction." IEEE Transactions on Multimedia 17, no. 11 (2015): 1949-1959. Paper

[56] Sudowe, Patrick, and Bastian Leibe. "PatchIt: Self-Supervised Network Weight Initialization for Fine-grained Recognition" In BMVC. 2016.

[57]